library(readr) # for loading data
library(tidyr) # for organizing data
library(ggplot2) # for graphing
library(lubridate) # for dates
library(gridExtra)
library(ggpubr)
library(MetBrewer) # for color palletes
library(dplyr) # for cleaner analysis
library(kableExtra) # for tables
library(stringr)Graphing When Wolves Fish in Northern Minnesota
Rationale
Visualize the time of day and time of year wolves are fishing in the Greater Voyageurs Ecosystem.
Packages Needed
Location Data
data<-readr::read_csv("/Users/danifreund/Desktop/fishing_data_code_for_publication/wolves_fishing/data/locations_by_fishing_period.csv", show_col_types = FALSE)
# getting hour by itself
# getting a time column hour:minute
data$time <- format(as.POSIXct(data$date.LMT), format = "%H:%M")
# getting just hour, have to use as.numeric for graphs to work
data$hour <- format(as.POSIXct(data$date.LMT), format = "%H") %>% as.numeric()
# making sure data matches fishing periods
data %>% subset(period=="during") %>% group_by(wolf) %>% summarize(start=min(date),end=max(date)) %>% kbl() %>% kable_material_dark(c("striped", "hover"))Remote Camera Data
camera <- read.csv("/Users/danifreund/Desktop/fishing_data_code_for_publication/wolves_fishing/data/camera_trap.csv") %>% data.frame()
# getting hour by itself
# removing cameras that do not have times recorded
camera$First_Time<-ifelse(camera$First_Time=="", NA, camera$First_Time)
camera$Last_Time<-ifelse(camera$Last_Time=="", NA, camera$Last_Time)
camera<-camera[complete.cases(camera[, c("First_Time")]), ]
# converting last date to date format
# has to be right format in csv file first!
camera$Last_Date <- format(as.POSIXct(camera$Last_Date), format = "%Y-%m-%d")
# combining date and time column because r is stupid and hates hours by themselves without dates
camera$date<-as.POSIXct(paste(camera$Last_Date, camera$Last_Time), format="%Y-%m-%d %H:%M:%S")
# getting just hour, have to use as.numeric for graphs to work
camera$hour <- format(as.POSIXct(camera$date), format = "%H") %>% as.numeric()
# getting rid of V067's one video of wading in Irwin Creek in 2018
camera <- subset(camera, !(Wolf_ID == "V067" & Tag_Type == "Globalstar"))
# making sure data matches fishing periods
camera %>% group_by(wolf=Wolf_ID, year=year(First_Date)) %>% summarize(start=min(First_Date),end=max(Last_Date)) %>% kbl() %>% kable_material_dark(c("striped", "hover"))| wolf | year | start | end |
|---|---|---|---|
| Unknown | 2018 | 2018-05-07 | 2018-05-16 |
| Unknown | 2019 | 2019-04-29 | 2019-05-21 |
| V034 | 2019 | 2019-05-04 | 2019-05-21 |
| V060 | 2018 | 2018-05-12 | 2018-05-18 |
| V067 | 2019 | 2019-04-29 | 2019-05-19 |
Getting Data Ready to Graph
# location data
data_clean <- data %>%
subset(period=="during") %>%
select(date.LMT, wolf, buffer, hour)
data_clean <- select(data_clean, wolf, hour, date.LMT, buffer)
# renaming columns
colnames(data_clean)<- c("wolf", "hour", "date.LMT", "Data")
# changing 20 to 20m Collar Locations
data_clean$Data<-gsub(20,"20m Collar Locations",as.character(data_clean$Data))
# changing 500 to 500m Collar Locations
data_clean$Data<-gsub(500,"500m Collar Locations",as.character(data_clean$Data))
# camera data
camera_clean <- camera %>%
select(Wolf_ID, date, hour)
# renaming columns
colnames(camera_clean)<- c("wolf", "date.LMT", "hour")
# adding camera column
camera_clean$Data<-"Remote Camera"
# combining data
locs_camera<-rbind(data_clean,camera_clean)
locs_camera$date<-date(locs_camera$date.LMT)
# getting month
locs_camera$month <- month(locs_camera$date.LMT)
# making a common year
locs_camera$month.day <- as.Date(format(locs_camera$date.LMT, "2022-%m-%d"))Making the Graphs
Graphs with Droopy Ends for Reference
20m Hour Plot
# creating color pallettes
my_colors_noid <- met.brewer("Signac")[c(2,3,4,5,6,8,10,11,12,13,14)]
my_colors <- met.brewer("Signac")[c(2,4,5,6,8,10,12,13,14)]
camera_20m <- locs_camera %>% subset(Data == "Remote Camera"|Data== "20m Collar Locations")
(plot.20mlocs.camera = (ggplot(camera_20m, aes(x = hour)) +
# density for all wolves together
geom_density(data=camera_20m,
aes(x=hour),
bw=2,
fill='dark gray', color='dark gray'))+
# standardizing the smoothing factor
# individual density plots for wolves
geom_density(aes(color=factor(wolf)#,linetype=Data
),
bw=2)+ labs(colour="Wolf ID")+
# standardizing the smoothing factor
# making the graph look pretty
coord_cartesian(xlim=c(0, 24), ylim=c(0,0.2), expand=0) +
labs(x="Hour of Day",
y=expression(atop("Density of 20 m Locations",
paste("and Camera Trap Events"),
color="Wolf ID")))+
theme_classic() +
theme(axis.text=element_text(size=15),
axis.title = element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent"),
# Legend stuff
legend.title = element_text(size=15, face="bold"),
legend.text=element_text(size=15),
legend.position = "bottom", legend.box="vertical") +
scale_color_manual(values = my_colors_noid))+
# editing legend title
guides(colour = guide_legend(nrow = 2, ncol = 11)) 
20m Month Plot
# making a common year
(plot.20.month = (ggplot(camera_20m, aes(month.day))+
geom_density(bw=5, fill='dark gray', color='dark gray')+
geom_density(aes(color=wolf#, linetype=year
), bw=5)) +
theme_classic()+
labs(x="Month",
y=expression(atop("Density of 20 m Locations",
paste("and Camera Trap Events"),
color="Wolf ID")))+
theme_classic() +
coord_cartesian(expand=0)+
scale_x_date(date_breaks="1 month", date_labels="%B",
limits = c(as.Date("2022-03-20"), as.Date("2022-08-15")))+
scale_color_manual(values = my_colors_noid)+
theme(axis.text=element_text(size=15),
#axis.text.x = element_text(angle=45,hjust=1),
axis.title=element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent"))) 
500m Hour Plot
data.500 <- subset(locs_camera, locs_camera$Data=="500m Collar Locations")
(plot.500 = (ggplot(data.500, aes(x = hour)) +
# density for all wolves together
geom_density(data=data.500,
aes(x=hour),
bw=2,
fill='dark gray', color='dark gray')+
# standardizing the smoothing factor
# individual density plots for wolves
geom_density(aes(color=factor(wolf)),
bw=2))+
# standardizing the smoothing factor
# making the graph look pretty
coord_cartesian(xlim=c(0, 24), ylim=c(0,0.16), expand=0) +
labs(x="Hour of Day",
y="Density of 500 m Locations",
color="Wolf ID")+
theme_classic()+
scale_color_manual(values = my_colors)+
theme(axis.text=element_text(size=15),
axis.title=element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent")))
500m Month Plot
# Get unique values in the "wolf" column
unique_wolves <- unique(data.500$wolf)
(plot.500.month = (ggplot(data.500, aes(month.day))+
geom_density(fill='dark gray', color='dark gray', bw=5,
show.legend = TRUE)+
geom_density(aes(color=wolf), bw=5,
show.legend = TRUE)) + theme_classic() +
labs(x="Month",
y="Density of 500 m Locations",
color="Wolf ID") +
coord_cartesian(expand=0)+
scale_x_date(date_breaks="1 month", date_labels="%B",
limits = c(as.Date("2022-03-20"), as.Date("2022-08-15")))+
scale_color_manual(values = my_colors)+
theme(axis.text=element_text(size=15),
#axis.text.x = element_text(angle=45,hjust=1),
axis.title=element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent")))
Tripling Data to Better Visualize Circularity of Time of Day Wolves Fished
Tripling the data to create a circular histogram can help to approximate circular data, such as the time of day wolves fish. The time of year wolves fish is not best displayed as circular data in this case because we only observed wolves fishing in the spring and the data we used only included ~3 months of the year. Consequently, we do not want to visualize the entire year of wolf locations.
We are not doing any statistics or even math with this data, we are just interested in visualizing the time of day and year that wolves fish. The graphs above show the data in it’s raw form without tripling for reference. We can see from these graphs that wolves are fishing for the most part in the spring and at night. Our data set is small (n = 10 collared wolves, and an unknown number of uncolored wolves) so we are hesitant to do any statistics beyond observing the patterns presented here.
Tripling the Data
# duplicating data so we can make a graph that is circular
# data 24 hours and 12 months before
locs_camera_1 <- locs_camera
locs_camera_1$hour <- locs_camera_1$hour-24
locs_camera_1$month <- locs_camera_1$month-12
# data 24 hours and 12 months after
locs_camera_2 <- locs_camera
locs_camera_2$hour <- locs_camera_2$hour+24
locs_camera_2$month <- locs_camera_2$month+12
# checking to make sure it worked
hour.1 <- locs_camera_1$hour
hour <- locs_camera$hour
hour.2 <- locs_camera_2$hour
hour.all<-cbind(hour.1, hour, hour.2) %>% data.frame()
month.1 <- locs_camera_1$month
month <- locs_camera$month
month.2 <- locs_camera_2$month
month.all<-cbind(month.1, month, month.2) %>% data.frame()
# finding differences between
hour.all$hour-hour.all$hour.1 # all = 24 = good
hour.all$hour-hour.all$hour.2 # all = -12 = good
month.all$month-month.all$month.1 # all = 12 = good
month.all$month-month.all$month.2 # all = -12 = good
# combining all data sets, should have 19290 rows
locs_camera_all <- rbind(locs_camera, locs_camera_1, locs_camera_2)Graphs without Droopy Ends for Manuscript
20m Hour Plot
# creating color pallettes
my_colors_noid <- met.brewer("Signac")[c(2,3,4,5,6,8,10,11,12,13,14)]
my_colors <- met.brewer("Signac")[c(2,4,5,6,8,10,12,13,14)]
camera_20m <- locs_camera_all %>% subset(Data == "Remote Camera"|Data== "20m Collar Locations")
(plot.20mlocs.camera = (ggplot(camera_20m, aes(x = hour)) +
# density for all wolves together
geom_density(data=camera_20m,
aes(x=hour),
bw=2,
fill='dark gray', color='dark gray'))+
# standardizing the smoothing factor
# individual density plots for wolves
geom_density(aes(color=factor(wolf)#,linetype=Data
),
bw=2)+ labs(colour="Wolf ID")+
# standardizing the smoothing factor
# making the graph look pretty
coord_cartesian(xlim=c(0, 24), ylim=c(0,0.06), expand=0) +
labs(x="Hour of Day",
y=expression(atop("Density of 20 m Locations",
paste("and Camera Trap Events"),
color="Wolf ID")))+
theme_classic() +
theme(axis.text=element_text(size=15),
axis.title = element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent"),
# Legend stuff
legend.title = element_text(size=15, face="bold"),
legend.text=element_text(size=15),
legend.position = "bottom", legend.box="vertical") +
scale_color_manual(values = my_colors_noid))+
# editing legend title
guides(colour = guide_legend(nrow = 2, ncol = 11)) 
500m Hour Plot
data.500 <- subset(locs_camera_all, locs_camera_all$Data=="500m Collar Locations")
(plot.500 = (ggplot(data.500, aes(x = hour)) +
# density for all wolves together
geom_density(data=data.500,
aes(x=hour),
bw=2,
fill='dark gray', color='dark gray')+
# standardizing the smoothing factor
# individual density plots for wolves
geom_density(aes(color=factor(wolf)),
bw=2))+
# standardizing the smoothing factor
# making the graph look pretty
coord_cartesian(xlim=c(0, 24), ylim=c(0,0.05), expand=0) +
labs(x="Hour of Day",
y="Density of 500 m Locations",
color="Wolf ID")+
theme_classic()+
scale_color_manual(values = my_colors)+
theme(axis.text=element_text(size=15),
axis.title=element_text(size=15),
rect = element_rect(fill="transparent"),
panel.background = element_rect(fill = "transparent")))
Tallying Number of Locations for each Wolf within 500m and 20m Buffers
table <- data %>% group_by(wolf,period,buffer) %>%
summarize(n=n()) %>%
pivot_wider(names_from = period, values_from = n)
table<-data.frame(table)
table$sum <- (sum = rowSums(table[, c("after", "during", "before")], na.rm = TRUE))
table <- table %>% mutate(percent.before = (before/sum)*100) %>%
mutate(percent.during = (during/sum)*100) %>%
mutate(percent.after = (after/sum)*100)
table$percent.after <- round(c(table$percent.after), 0)
table$percent.during <- round(c(table$percent.during), 0)
table$percent.before <- round(c(table$percent.before), 0)
#table %>% kbl(caption = "Percent locs out of locs within 20 and 500 buffers") %>% kable_material_dark(c("striped", "hover"))# loading wolf locations
all_locs <- read_csv(file = "/Users/danifreund/Desktop/fishing_data_code_for_publication/ethology_of_wolves_freshwater_fishing/data/all.locs.fishing.period.no.buffers.csv", show_col_types = FALSE)
fishing_period <- read_csv(file="/Users/danifreund/Desktop/fishing_data_code_for_publication/ethology_of_wolves_freshwater_fishing/data/summary_fishing_periods_final.csv", show_col_types = FALSE)
# clipping all locaitons to fishing period
fishing_period$start <- as.Date(fishing_period$start, format = "%m/%d/%y")
fishing_period$end <- as.Date(fishing_period$end, format = "%m/%d/%y")
fishing_period$start.before.fishing <- (fishing_period$start - fishing_period$length)
fishing_period$end.after.fishing <- (fishing_period$end + fishing_period$length)
all_locs <- all_locs %>%
inner_join(fishing_period, by = "wolf") %>%
filter(date >= start.before.fishing, date <= end.after.fishing)
clipped.locs <- all_locs %>% group_by(wolf) %>% summarize(min.all.locs = min(date),max.all.locs = max(date))
fishing_period <- merge(fishing_period, clipped.locs, by="wolf")
# seeing if we have missing locs
fishing_period$match.start <- ifelse(fishing_period$start.before.fishing == fishing_period$min.all.locs, "no missing locs", "missing locs")
fishing_period$match.end <- ifelse(fishing_period$end.after.fishing == fishing_period$max.all.locs, "no missing locs", "missing locs")# percent locs spent fishing
# calculating all locs put out before, during, and after the fishing period regardless of if they are in the buffer or not
table.all.locs <- all_locs %>% group_by(wolf, period) %>% summarize(total=n()) %>% pivot_wider(names_from = period,
values_from = total)
# re-naming to have all in title
colnames(table.all.locs) <- c("wolf","all_after"
,"all_during","all_before")
# calculating all locs put out before, during, and after the fishing period within buffers
buffered.locs <- data %>% group_by(wolf,buffer,period) %>% summarize(n=n()) %>%
pivot_wider(names_from = c(period,buffer),
values_from = n)
# merging data frames
table.1 <- merge(table.all.locs, buffered.locs, by = "wolf")
# dividing locs within buffers by all locs put out at that time
table.1$percent.20.before <- (table.1$before_20/table.1$all_before)
table.1$percent.20.during <- (table.1$during_20/table.1$all_during)
table.1$percent.20.after <- (table.1$after_20/table.1$all_after)
table.1$percent.500.before <- (table.1$before_500/table.1$all_before)
table.1$percent.500.during <- (table.1$during_500/table.1$all_during)
table.1$percent.500.after <- (table.1$after_500/table.1$all_after)
# adding collar fix intervals
table.1$collar.fix <- ifelse(table.1$wolf == "V034" | table.1$wolf == "V060","12 hour","20 min")
# rounding the values
table.1 <- table.1 %>% mutate_at(vars(11:16), round, digits = 2)
# just selecting the percents
table.1.percent <- (table.1[,c(1,11:17)])
# pivoting longer
table.percent.long <- table.1.percent %>% pivot_longer(cols = 2:7,
names_to = "variable",
values_to = "value")
table.percent.long <- table.percent.long %>% tidyr::separate(variable, c('percent','Buffer',"Period"))
# re-naming fishing periods
table.percent.long$Period <- ifelse(table.percent.long$Period =="before", "Before Fishing Period", table.percent.long$Period)
table.percent.long$Period <- ifelse(table.percent.long$Period =="during", "During Fishing Period", table.percent.long$Period)
table.percent.long$Period <- ifelse(table.percent.long$Period =="after", "After Fishing Period", table.percent.long$Period)
# calculating average
average <- table.percent.long %>% group_by(Buffer,Period) %>% summarize(value = ((mean(value, na.rm=T))))
# calculating sd
sd <- table.percent.long %>%group_by(Buffer, Period) %>% summarize(sd = sd(value, na.rm=T))
# merging data sets
average <- merge(average, sd, by=c("Period","Buffer"))
# making a wide version of the average data
means <- colMeans(table.1.percent[,2:7], na.rm=T)
means <- data.frame(t(means))
means$wolf <- "average"
means$collar.fix <- NA
sd_vals <- sapply(table.1.percent, sd, na.rm=T)
sd_vals <- data.frame(t(sd_vals))
sd_vals$wolf <- "SD"
sd_vals$collar.fix <- NA
table.1.percent.average <- rbind(table.1.percent, means)
table.1.percent.average.sd <- rbind(table.1.percent.average, sd_vals)
table.1.percent.average.sd[,2:7]<-round(table.1.percent.average.sd[,2:7] * 100, 2)
table.1.percent.average.sd %>% kbl(caption = "Percent locs within buffers out of all locs before, during, and after fishing periods") %>% kable_material_dark(c("striped", "hover"))| wolf | percent.20.before | percent.20.during | percent.20.after | percent.500.before | percent.500.during | percent.500.after | collar.fix |
|---|---|---|---|---|---|---|---|
| O0C | NA | 2.00 | 1.00 | NA | 16.00 | 10.00 | 20 min |
| V034 | NA | 23.00 | 9.00 | 5.00 | 50.00 | 9.00 | 12 hour |
| V046 | 1.00 | 14.00 | 2.00 | 6.00 | 48.00 | 7.00 | 20 min |
| V060 | 38.00 | 40.00 | NA | 62.00 | 80.00 | NA | 12 hour |
| V062 | NA | 4.00 | 1.00 | 5.00 | 22.00 | 10.00 | 20 min |
| V071 | 4.00 | 1.00 | 0.00 | 6.00 | 17.00 | 1.00 | 20 min |
| V077 | 9.00 | 20.00 | 2.00 | 80.00 | 91.00 | 70.00 | 20 min |
| V089 | 6.00 | 10.00 | 6.00 | 17.00 | 59.00 | 36.00 | 20 min |
| V094 | 1.00 | 7.00 | 0.00 | 6.00 | 24.00 | 9.00 | 20 min |
| average | 9.83 | 13.44 | 2.62 | 23.38 | 45.22 | 19.00 | NA |
| SD | 14.13 | 12.59 | 3.20 | 30.05 | 27.76 | 23.05 | NA |
average.wide <- average %>% pivot_wider(names_from = c(Period,Buffer),
values_from = c(value, sd))# hours spent fishing
# just selecting the counts
table.1.count <- (table.1[,c(1:10,17)])
# adding fishing period length
fishing_period_days <- select(fishing_period, wolf, length)
table.1.count <- merge(table.1.count, fishing_period_days, by = "wolf")
# subsetting to just get 20 min interval wolves
collar.20.min<-subset(table.1.count, table.1.count$collar.fix=="20 min")
# calculating the hours
# duplicating the data for 20 min
collar.20.min.hours <- collar.20.min
collar.20.min.hours[,2:10] <- collar.20.min.hours[,2:10]*20 # calculating min
collar.20.min.hours[,2:10] <- collar.20.min.hours[,2:10]/60 # calculating hours
collar.20.min.hours[,2:10] <- collar.20.min.hours[,2:10]/collar.20.min.hours$length # calculating the number of hours per day of the length of the fishing period
# calculating mean
means <- sapply(collar.20.min.hours, mean, na.rm=T)
means <- data.frame(t(means))
means$wolf <- "mean"
# calculating sd
sd_vals <- sapply(collar.20.min.hours, sd, na.rm=T)
sd_vals <- data.frame(t(sd_vals))
sd_vals$wolf <- "sd"
collar.20.min.hours<-rbind(collar.20.min.hours,means,sd_vals)
collar.20.min.hours[,c(2:10,12)]<-(round(collar.20.min.hours[,c(2:10,12)],2)) # rounding to have no decimal pionts
# creating table
collar.20.min.hours %>% select("Wolf ID" = wolf,
"GPS Collar" = collar.fix,
"Before w/in 20" = before_20,
"During w/in 20" = during_20,
"After w/in 20" = after_20,
"Before w/in 500" = before_500,
"During w/in 500" = during_500,
"After w/in 500" = after_500) %>% kbl(caption = "Hours spent fishing per day within buffers at fishing water sources") %>% kable_material_dark(c("striped", "hover"))| Wolf ID | GPS Collar | Before w/in 20 | During w/in 20 | After w/in 20 | Before w/in 500 | During w/in 500 | After w/in 500 | |
|---|---|---|---|---|---|---|---|---|
| 1 | O0C | 20 min | NA | 0.48 | 0.32 | NA | 3.79 | 2.45 |
| 4 | V046 | 20 min | 0.29 | 3.31 | 0.48 | 1.56 | 11.60 | 1.71 |
| 6 | V062 | 20 min | NA | 1.08 | 0.15 | 0.40 | 5.48 | 2.52 |
| 7 | V071 | 20 min | 0.39 | 0.20 | 0.09 | 0.67 | 4.14 | 0.28 |
| 8 | V077 | 20 min | 2.43 | 5.17 | 0.63 | 21.00 | 23.80 | 17.67 |
| 9 | V089 | 20 min | 1.50 | 2.54 | 1.58 | 4.50 | 15.33 | 9.21 |
| 10 | V094 | 20 min | 0.29 | 2.00 | 0.10 | 1.52 | 6.43 | 2.48 |
| 11 | mean | NA | 0.98 | 2.11 | 0.48 | 4.94 | 10.08 | 5.19 |
| 12 | sd | NA | 0.96 | 1.75 | 0.53 | 8.00 | 7.39 | 6.19 |
# graphing the data
# proportion locs
library(scales)
# line graphs
# subsetting the data
buff.20<-subset(table.percent.long, table.percent.long$Buffer == 20)
# Calculate the average value for each Period
avg_values <- buff.20 %>%
group_by(Period) %>%
summarize(avg_value = mean(value, na.rm=T))
# graphing the data
buff.20 %>% ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value,
colour = wolf,
group=wolf,
linetype = factor(collar.fix,
levels = c("20 min", "12 hour")))) +
scale_y_continuous(labels = percent_format()) +
geom_line() +
geom_point() +
theme_classic() +
labs(x = "Time Period",
y = "Percent of Locations within 20 m Buffer", colour = "Wolf ID") +
scale_linetype_manual(values = c(1, 2),
guide =
guide_legend(title = "GPS Collar Fix Interval")) 
subset(average, Buffer==20) %>% ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value)) +
scale_y_continuous(labels = percent_format()) +
geom_line(aes(group=1)) +
geom_point() +
theme_classic() +
geom_errorbar(aes(ymin= value - sd, ymax=value + sd), width = 0.2) +
labs(x = "Time Period",
y = "Average Percent of Locations within 20 m Buffer", colour = "Average")
# 500 m graph
buff.500<-subset(table.percent.long, table.percent.long$Buffer == 500)
buff.500 %>% ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value,
colour = wolf,
group=wolf,
linetype = factor(collar.fix,
levels = c("20 min", "12 hour")))) +
scale_y_continuous(labels = percent_format()) +
geom_line() +
geom_point() +
theme_classic() +
labs(x = "Time Period",
y = "Percent of Locations within 500 m Buffer",
colour = "Wolf ID") +
scale_linetype_manual(values = c(1, 2),
guide = guide_legend(title = "GPS Collar Fix Interval")) 
subset(average, Buffer==500) %>% ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value)) +
scale_y_continuous(labels = percent_format()) +
geom_line(aes(group=1)) +
geom_point() +
theme_classic() +
geom_errorbar(aes(ymin= value - sd, ymax=value + sd), width = 0.2) +
labs(x = "Time Period",
y = "Average Percent of Locations within 500 m Buffer", colour = "Average")
# graphing hours for 20 min interval wolves
library(scales)
# line graphs
# creating color pallettes
my_colors <- c("#808080", met.brewer("Signac")[c(2,5,8,10,12,13,14)])
# pivoting values longer
collar.20.min.hours.select <- collar.20.min.hours[,c(1,5:10)]
table.prop.hours.long <- collar.20.min.hours.select %>% pivot_longer(cols = 2:7,
names_to = "variable",
values_to = "value")
# seperating labels
table.prop.hours.long <- table.prop.hours.long %>% tidyr::separate(variable, c("Period",'Buffer'))
# re-naming things
table.prop.hours.long$Period <- ifelse(table.prop.hours.long$Period == "before", "Before Fishing Period", table.prop.hours.long$Period)
table.prop.hours.long$Period <- ifelse(table.prop.hours.long$Period == "during", "During Fishing Period", table.prop.hours.long$Period)
table.prop.hours.long$Period <- ifelse(table.prop.hours.long$Period == "after", "After Fishing Period", table.prop.hours.long$Period)
# graphing the data
# 20 m buffer
subset(table.prop.hours.long, table.prop.hours.long$Buffer==20 &
table.prop.hours.long$wolf!= "sd") %>%
ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value,
colour = wolf,
group=wolf)) +
geom_line(aes(linetype = ifelse(wolf != "mean", "dotted", "solid"))) +
geom_point() +
scale_color_manual(values = my_colors) +
theme_classic() +
labs(x = "Time Period",
y = "Hours Per Day Spent within 20 m of Fishing Water Source", colour = "Wolf ID") +
scale_linetype_manual(values = c(1, 2),
guide =
guide_legend(title = "Mean vs. Individual")) 
# 500 m buffer
subset(table.prop.hours.long, table.prop.hours.long$Buffer==500 &
table.prop.hours.long$wolf!= "sd") %>%
ggplot(aes(x = factor(Period,
levels = c("Before Fishing Period",
"During Fishing Period",
"After Fishing Period")),
y = value,
colour = wolf,
group=wolf)) +
geom_line(aes(linetype = ifelse(wolf == "mean", "solid", "dotted"))) +
geom_point() +
theme_classic() +
scale_color_manual(values = my_colors) +
labs(x = "Time Period",
y = "Hours Per Day Spent within 500 m of Fishing Water Source", colour = "Wolf ID") + scale_linetype_manual(values = c(1, 2),
guide =
guide_legend(title = "Mean vs. Individual")) 